Data mining, atau penambangan data merupakan proses pengumpulan dan pengolahan data untuk mengekstrak informasi penting. Tahapan dalam proses data mining berguna untuk mencari sebuah pola tertentu dari data penilaian yang sangat banyak. Tujuan ini yaitu mengetahui dan membentuk cluster data siswa berdasarkan nilai sehingga menjadi sebuah cluster, sehingga hasil cluster siswa dapat menjadi acuan dalam meningkatkan nilai siswa dalam proses pembelajaran selanjutnya. Hasil evaluasi dan penilaian terhadap siswa dilakukan oleh tenaga pengajar atau guru dalam melakukan penilaian selama proses pembelajaran. Dalam proses pembelajaran terdapat 2 kategori penilaian yaitu nilai siswa UTS serta UAS. Hasil pengelompokan data nilai siswa menggunakan metode K-Means clustering menunjukan bahwa berdasarkan hasil cluster data siswa dalam satu semester, maka didapatkan cluster 0 berjumlah 7 siswa, cluster 1 berjumlah 3. Hasil pengujian menggunakan rapid miner maka terdapat 7 siswa yang memiliki nilai dengan rata-rata yang baik dan terdapat 3 siswa dengan rata-rata nilai yang kurang baik.

Prediksi dengan model sistem pendukung keputusan merupakan cara yang tepat sasaran untuk digunakan dalam memecahkan masalah. Demam Berdarah Dengue (DBD) adalah penyakit endemik di Indonesia yang memerlukan penanganan cepat untuk mencegah komplikasi lebih lanjut. Prediksi diagnosis DBD dengan menggunakan algoritma Decision Tree C4.5 memiliki tingkat akurasi 100% dan meyakinkan. Dataset yang digunakan mencakup data medis pasien, seperti gejala klinis yaitu demam, nyeri sendi, mual, hasil laboratorium berupa trombosit, hematokrit, uji NS1, serta riwayat komorbiditas dan durasi gejala. Proses pre-processing dilakukan untuk memastikan data siap digunakan, dengan menangani data yang hilang dan menyesuaikan format data agar konsisten. Model Decision Tree C4.5 dipilih karena kemampuannya mengolah data dengan berbagai format dan hasilnya dapat dengan mudah dipahami. Model C4.5 dievaluasi menggunakan metrik akurasi, presisi, sensitivitas, dan spesifisitas. Dengan performa yang baik, model ini memiliki potensi untuk digunakan dalam sistem pendukung keputusan medis. Implementasinya di lapangan dapat membantu tenaga medis dalam mempercepat diagnosis dan memberikan penanganan yang lebih tepat waktu, yang sangat penting dalam menangani pasien DBD.

SM Wedding Decoration is a place that provides services to take care of everything related to weddings. For example, wedding decorations, wedding organizers, and wedding planners. SM Wedding Decoration has several wedding packages that can be offered to customers.  The large number of packages available makes prospective brides or customers confused about which wedding package is suitable for their wedding. The a priori algorithm method is used in this research to find recommendations for wedding packages based on existing transaction data and to improve company strategies and sales of other wedding packages. The Apriori algorithm is used to help computers learn patterns of association rules. This algorithm looks for a group of things that match the given criteria or order and have a certain frequency value. From this research, customers tend to order Photographer & Documentation and MUA ? Deluxe packages more often, and these orders account for 44% of all package order transaction data. Transaction data for ordering the MUA?Deluxe Package was 41.3%. Photographer & Documentation package transaction data ? Deluxe Package is 41.2%. And transaction data for ordering the MUA package ? Premium Deluxe Package is 41.3%.

The application of data mining in the evaluation of Hadrah tests at Pesantren Salafiyah Syafi'iyah is explored using the Random Forest algorithm. Hadrah, a form of Islamic artistic performance involving vocal and percussion elements, is integral to the cultural and spiritual life in Islamic boarding schools. The objective of this research is to enhance the accuracy and objectivity of performance assessments in Hadrah, particularly in the context of competition or educational evaluation at Pesantren Salafiyah Syafi'iyah. By utilizing the Random Forest method, which is a robust machine learning technique, the study aims to minimize the subjectivity and inconsistency inherent in traditional evaluation methods. The study leverages secondary data from previous Hadrah tests, applying preprocessing steps to ensure the data is suitable for analysis. The results show that Random Forest provides a high level of precision in classifying participants based on key assessment features such as tempo, consistency, and overall performance. This method contributes significantly to improving the reliability and fairness of the evaluation process, ensuring a more standardized approach to assessing artistic skills in the context of Islamic traditions. The findings suggest that data-driven approaches can play a pivotal role in preserving and promoting Islamic arts while enhancing the educational process.

This research aims to increase efficiency in monitoring student attendance at Madrasah I'dadiyah Salafiyah Syafi'iyah by utilizing the K-Means Clustering analysis method. Monitoring student attendance is still carried out conventionally, so it often takes time and is less effective in identifying overall student attendance patterns. For this reason, in this research, student attendance data collected from the madrasa attendance system was analyzed using K-Means Clustering, a machine learning technique that can group students based on their attendance patterns. This process produces several groups which make it easier for the madrasah to identify students who frequently attend, rarely attend, or frequently do not attend. In this way, madrasas can take more appropriate steps in dealing with attendance problems, such as paying special attention to students who are often absent. The results of this research indicate that the application of K-Means Clustering can increase the efficiency of attendance monitoring and provide a stronger basis for decision making to improve the attendance system at the I'dadiyah Salafiyah Syafi'iyah madrasah.

To improve the evaluation process in assessing student progress, predicting the best grades plays a crucial role in enhancing the quality of education. By identifying the top-performing students, educational institutions can refine their teaching methods and create targeted strategies to foster better learning outcomes. This step is vital for ensuring that the learning process aligns with the institution's goals to produce highly skilled and knowledgeable students. In this research, we focused on utilizing the C4.5 algorithm, a widely recognized decision tree method in data mining, to predict student achievements. The C4.5 algorithm is known for its ability to classify and uncover hidden patterns within datasets, making it a powerful tool for educational data analysis. Through this approach, we aim to analyze the factors influencing student success and provide actionable insights for educators and administrators. The study was conducted on students from Madrasah Ta’hiliyah Ibrahimy, where we applied the decision tree algorithm to predict the best grades based on historical academic data. The experiment resulted in three distinct rules or patterns derived from the data, with an overall accuracy of 74.17%. These findings demonstrate the potential of data-driven approaches in supporting academic decision-making and guiding future interventions to further enhance student performance.

Sleep disorders, including insomnia, can be influenced by various lifestyle factors, such as sleep duration, sleep quality, physical activity, and individual health conditions. This study aims to categorize the risk level of insomnia based on lifestyle using the K-Means clustering algorithm. The data used include sleep duration, sleep quality, heart rate, and daily step count. Through the implementation of the K-Means algorithm, the data is analyzed to group individuals into several categories based on existing lifestyle patterns. The results of the study show a correlation between a healthy lifestyle and better sleep quality. In addition, the resulting clusters provide insight into lifestyle characteristics that affect the risk of insomnia, so that they can be the basis for recommendations for more targeted health interventions. This study is expected to contribute to the development of data-based sleep disorder management strategies by utilizing machine learning methods, especially the K-Means algorithm, to support efforts to improve the quality of life of the community.

This study analyzes the performance patterns of students at Madrasah I’dadiyah Sukorejo using data mining methods, specifically clustering. The analyzed factors include exam scores and participation in extracurricular activities, as both are considered to significantly influence academic performance. Exam scores reflect mastery of subjects, while extracurricular activities often positively impact students' social skills and learning motivation.[1] The K-Means algorithm was selected to classify students into three main groups: high-performing, average-performing, and low-performing students. The clustering results are expected to provide strategic guidance for the school to improve the quality of education. Low-performing students can receive additional guidance or motivational training, while average-performing students can be encouraged to participate more actively in extracurricular activities to enhance interpersonal skills. Understanding these performance patterns helps the school design more effective programs to maximize students’ academic potential based on their needs. This study also opens opportunities for further exploration of other factors affecting academic performance, such as family conditions and the home learning environment. Thus, this approach becomes an essential step in creating a more inclusive and high-quality education system.

Entrepreneurs engaged in the shopping sector have promising prospects because they can serve the lower and upper middle classes and provide convenience for people to buy everyday goods without having to go to supermarkets or convenience stores. However, if the availability of goods or materials needed is not optimally guaranteed, there may be a shortage of goods or materials needed. This also happens in some stores, where customers often run out of stock of various products and equipment they are looking for, but this is due to the lack of inventory management habits in the store. In this case, it is about finding out what products and needs are needed by store customers. This dataset uses several variables such as transaction date, product name, and sales or purchase amount by applying the apriori algorithm. The apriori algorithm is a type of association rule in data mining that is used to analyze and find correlation patterns. The data used in this study is a sample of 100 sales transaction data. The final association rule obtained from the transaction data is "If consumers buy Flour, they will buy Oil and Eggs" with a support percentage of 54% and a confidence of 96%. These results provide data on the names of the best-selling products, which can be used as an inventory estimate to avoid empty seats that can result in customer disappointment.

Clustering is a data mining technique used for grouping data based on specific similarities. This study implements K-Means Clustering to analyze foreign tourist visit data in Indonesia in 2024. Using the Knowledge Discovery in Database (KDD) methodology, the research involves five stages: Data Selection, preprocessing, Transformation, data mining, and Evaluation. Data Clustering was conducted using RapidMiner software, experimenting with different cluster counts (k=2 to k=7) to determine the optimal number of clusters. Results indicate that three clusters (k=3) with the smallest Davies-Bouldin Index (DBI) value were optimal. This Clustering approach categorizes tourists into low, medium, and high visit groups, assisting policymakers in strategic tourism development. The findings support capacity planning and seasonal marketing strategies to optimize Indonesia's tourism sector.